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  • BO SUN

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    1. Peking University, China
    • Please address correspondence to: Bo Sun, Department of Guanghua School of Management, Peking University, New Guanghua building 346, Beijing 100871, China. Phone: +86-10-6276-7568. Fax: +86-10-6275-3590. E-mail:

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    • This article is a substantial revision of part of my dissertation at the University of Virginia; I gratefully acknowledge my advisors, Eric Young and Chris Otrok, for their guidance on this article. I thank Toshi Mukoyama and Mark Carey for timely suggestions and unwavering support. Part of the revision was conducted while I was at the Federal Reserve Board of Governors. This article has also benefited from comments of David Bowman, Hanming Fang (the editor), Antonio Falato, Jon Glover, Adriano Rampini, Cathy Schrand, Xuan Tam, two anonymous referees, and seminar participants at Boston University, Carnegie Mellon University, Cornell University, Chinese University of Hong Kong, Darden School of Business, Federal Reserve Board of Governors, Federal Reserve Bank of Cleveland, Federal Reserve Bank of Richmond, Georgetown University, Hong Kong University of Science and Technology, INSEAD, Peking University, SAIF, Tsinghua University, University of Chicago Booth School of Business, University of Hong Kong, University of Maryland, University of Tokyo, University of Virginia, Wharton School of Business, World Bank, 2010 Midwest Macroeconomic Meeting, and 2010 Society of Economic Dynamics Meeting. I acknowledge financial support from National Natural Science Foundation of China (Grant Nos. 71021001 and 71302029).


The article investigates stock return dynamics in an environment where executives have an incentive to maximize their compensation by artificially inflating earnings. A principal–agent model with financial reporting and managerial effort is embedded in a Lucas asset-pricing model with periodic revelations of the firm's underlying profitability. The return process generated from the model is consistent with a range of empirical regularities observed in the return data: volatility clustering, asymmetric volatility, and high idiosyncratic volatility. The calibration results further indicate that earnings management can be quantitatively important in accounting for the dynamic patterns of stock returns.